Let's Put Garbage-Can Regressions and Garbage-Can Probits Where They Belong

By Christopher H. Achen. 2005

524 citations recorded by the source corpus.

Subfields: Political Science and International Relations, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/81d65b6a072fa0e0e6c99f40a952bb09ae5db0af

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Citations in the corpus

The Phantom Menace: Omitted Variable Bias in Econometric Research (2005, background): “In his contribution to this volume, Achen (2005) shows that small amounts of nonlinearity in control variables can have deleterious effects, and Griliches (1977, 12) argues that small amounts of measurement error in control variables “are magnified as more variables are added to the equation in an a”
The Cost of Shame: International Organizations and Foreign Aid in the Punishing of Human Rights Violators (2009, methodology): “Third, we ran several leaner model specifications to disclose effects that are hidden when including large numbers of variables (Achen, 2005).”
Case Studies: Types, Designs, and Logics of Inference (2008, methodology): “…regression analyses that incorporate many dummy and control variables into the analysis of large populations, and instead focus on particular subsets of cases and limit the use of control variables (Achen, 2005; Ray, 2005), for example, reflect a sensitivity to scope conditions (Levy, 2007a).”
Citizens, legislators, and executive disclosure: the political determinants of fiscal transparency (2013, background): “The list of potential candidates is long, but we are mindful to avoid the trap of „garbage-can regressions‟ (Achen 2005) or „kitchen-sink models‟ (Schrodt 2010) in which swathes of poorly theorized and correlated „controls‟ are dumped on the right-hand side of regression equations.”
The Source of Turnout Decline (2013, background): “On the issue of “controlling” for auxiliary factors, see, for example, Achen (2002, 2005).”
Thinking Clearly About Correlations and Causation: Graphical Causal Models for Observational Data (2017, background): “Models resulting from such an approach have been labeled “garbage-can regressions” (Achen, 2005) because the idea that the inclusion of a multitude of control variables will necessarily improve (and will not worsen) causal inference is a methodological urban legend at best (Spector & Brannick,…”
Testing for Zero Inflation in Count Models: Bias Correction for the Vuong Test (2013, unknown): “In formulating statistical models, there is an inherent tension between reducing the data to a parsimonious and comprehensible summary and specifying a model that adequately captures the complexities in real data (Achen 2005).”
Toward a Network Theory of Alliance Formation (2012, unknown): “Additionally, as Achen (2002 , 2005 ) points out, there is a great deal of causal heterogeneity over such long time periods that would be difficult to control for in a statistical analysis.”